Merge pull request #343 from yasirali0/docs-correction

fixed minor grammatical and spelling errors

docker/Dockerfile

@@ -1,7 +1,7 @@
-# This is a example of fedlab installation via Dockerfile
+# This is an example of fedlab installation via Dockerfile
 
 # replace the value of TORCH_CONTAINER with pytorch image that satisfies your cuda version
-# you can finde it in https://hub.docker.com/r/pytorch/pytorch/tags
+# you can find it in https://hub.docker.com/r/pytorch/pytorch/tags
 ARG TORCH_CONTAINER=1.5-cuda10.1-cudnn7-runtime
 
 FROM pytorch/pytorch:${TORCH_CONTAINER}

docker/README.md

@@ -4,11 +4,11 @@
 
 - Step 1
 
-Find an appropriate base pytorch image for your platform from dockerhub https://hub.docker.com/r/pytorch/pytorch/tags. Then, replace the value of TORCH_CONTAINER in demo dockerfile.
+Find an appropriate base PyTorch image for your platform from dockerhub https://hub.docker.com/r/pytorch/pytorch/tags. Then, replace the value of TORCH_CONTAINER in demo dockerfile.
 
 - Step 2
 
-To install specific pytorch version, you need to choose a correct install command, which can be find in https://pytorch.org/get-started/previous-versions/. Then, modify the 16-th command in demo dockerfile.
+To install specific PyTorch version, you need to choose a correct install command, which can be found at https://pytorch.org/get-started/previous-versions/. Then, modify the 16-th command in demo dockerfile.
 
 - Step 3
 
@@ -17,8 +17,8 @@ Build the images for your own platform by running command below.
 
 - Note
 
-Please be sure using "--gpus all" and "--network=host" when start a docker contrainer:
+Please be sure to use "--gpus all" and "--network=host" when starting a docker container:
 
 > $ docker run -itd --gpus all --network=host b23a9c46cd04(image name) /bin/bash
 
-If you are not in China area, it is ok to remove line 11,12 and "-i https://pypi.mirrors.ustc.edu.cn/simple/" in line 19.
+If you are not in China area, it is ok to remove line 11, 12 and "-i https://pypi.mirrors.ustc.edu.cn/simple/" in line 19.

docs/source/overview.rst

@@ -8,7 +8,7 @@ Introduction
 
 Federated learning (FL), proposed by Google at the very beginning, is recently a burgeoning research area of machine learning, which aims to protect individual data privacy in distributed machine learning process, especially in finance, smart healthcare and edge computing. Different from traditional data-centered distributed machine learning, participants in FL setting utilize localized data to train local model, then leverages specific strategies with other participants to acquire the final model collaboratively, avoiding direct data sharing behavior.
 
-To relieve the burden of researchers in implementing FL algorithms and emancipate FL scientists from repetitive implementation of basic FL setting, we introduce highly customizable framework **FedLab** in this work. **FedLab** is builded on the top of `torch.distributed <https://pytorch.org/docs/stable/distributed.html>`_ modules and provides the necessary modules for FL simulation, including communication, compression, model optimization, data partition and other functional modules. **FedLab** users can build FL simulation environment with custom modules like playing with LEGO bricks. For better understanding and easy usage, FL algorithm benchmark implemented in **FedLab** are also presented.
+To relieve the burden of researchers in implementing FL algorithms and emancipate FL scientists from repetitive implementation of basic FL setting, we introduce a highly customizable framework **FedLab** in this work. **FedLab** is built on the top of `torch.distributed <https://pytorch.org/docs/stable/distributed.html>`_ modules and provides the necessary modules for FL simulation, including communication, compression, model optimization, data partition and other functional modules. **FedLab** users can build FL simulation environment with custom modules like playing with LEGO bricks. For better understanding and easy usage, FL algorithm benchmarks implemented in **FedLab** are also presented.
 
 For more details, please read our `full paper`__.
 
@@ -95,7 +95,7 @@ Experimental Scene
 Standalone
 -----------
 
-**FedLab** implements ``SerialTrainer`` for FL simulation in single system process. ``SerialTrainer`` allows user to simulate a FL system with multiple clients executing one by one in serial in one ``SerialTrainer``. It is designed for simulation in environment with limited computation resources.
+**FedLab** implements ``SerialTrainer`` for FL simulation in single system process. ``SerialTrainer`` allows user to simulate a FL system with multiple clients executing one by one in serial in one ``SerialTrainer``. It is designed for simulation in environment with limited computational resources.
 
 .. image:: ../imgs/fedlab-SerialTrainer.svg
    :align: center
@@ -108,7 +108,7 @@ Standalone
 Cross-process
 -------------
 
-**FedLab** enables FL simulation tasks to be deployed on multiple processes with correct network configuration (these processes can be run on single or multiple machines). More flexibly in parallel, ``SerialTrainer`` can replace the regular ``Trainer`` directly. Users can balance the calculation burden among processes by choosing different ``Trainer``. In practice, machines with more computation resources can be assigned with more workload of calculation.
+**FedLab** enables FL simulation tasks to be deployed on multiple processes with correct network configuration (these processes can be run on single or multiple machines). More flexibly in parallel, ``SerialTrainer`` can replace the regular ``Trainer`` directly. Users can balance the calculation burden among processes by choosing different ``Trainer``. In practice, machines with more computational resources can be assigned with more workload of calculation.
 
 .. note::
 

docs/source/tutorials/dataset_partition.rst

@@ -4,7 +4,7 @@
 Federated Dataset and DataPartitioner
 *************************************
 
-Sophisticated in real world, FL need to handle various kind of data distribution scenarios, including
+Sophisticated in real world, FL needs to handle various kinds of data distribution scenarios, including
 iid and non-iid scenarios. Though there already exists some datasets and partition schemes for published data benchmark,
 it still can be very messy and hard for researchers to partition datasets according to their specific
 research problems, and maintain partition results during simulation. FedLab provides :class:`fedlab.utils.dataset.partition.DataPartitioner` that allows you to use pre-partitioned datasets as well as your own data. :class:`DataPartitioner` stores sample indices for each client given a data partition scheme. Also, FedLab provides some extra datasets that are used in current FL researches while not provided by official Pytorch :class:`torchvision.datasets` yet.

docs/source/tutorials/distributed_communication.rst

@@ -8,9 +8,9 @@ Distributed Communication
 Initialize distributed network
 ======================================
 
-FedLab uses `torch.distributed <https://pytorch.org/docs/stable/distributed.html>`_ as point-to-point communication tools. The communication backend is Gloo as default. FedLab processes send/receive data through TCP network connection. Here is the details of how to initialize the distributed network. 
+FedLab uses `torch.distributed <https://pytorch.org/docs/stable/distributed.html>`_ as point-to-point communication tools. The communication backend is Gloo as default. FedLab processes send/receive data through TCP network connection. Here are the details of how to initialize the distributed network. 
 
-You need to assign right ethernet to :class:`DistNetwork`, making sure ``torch.distributed`` network initialization works. :class:`DistNetwork` is for quickly network configuration, which you can create one as follows:
+You need to assign right ethernet to :class:`DistNetwork`, making sure ``torch.distributed`` network initialization works. :class:`DistNetwork` is for quick network configuration, which you can create as follows:
 
 .. code-block:: python
 
@@ -29,7 +29,7 @@ You need to assign right ethernet to :class:`DistNetwork`, making sure ``torch.d
 - Make sure ``world_size`` is the same across process.
 - Rank should be different (from ``0`` to ``world_size-1``).
 - world_size = 1 (server) + client number.
-- The ethernet is None as default. torch.distributed will try finding the right ethernet automatically. 
+- The ethernet is None as default. ``torch.distributed`` will try finding the right ethernet automatically. 
 - The ``ethernet_name`` must be checked (using ``ifconfig``). Otherwise, network initialization would fail.
 
 If the automatically detected interface does not work, users are required to assign a right network interface for Gloo, by assigning in code or setting the environment variables ``GLOO_SOCKET_IFNAME``, for example ``export GLOO_SOCKET_IFNAME=eth0`` or ``os.environ['GLOO_SOCKET_IFNAME'] = "eth0"``.
@@ -45,7 +45,7 @@ If the automatically detected interface does not work, users are required to ass
 Point-to-point communication
 =============================
 
-In recent update, we hide the communication details from user and provide simple APIs. :class:`DistNetwork` now provies two basic communication APIs: :meth:`send()` and :meth:`recv()`. These APIs suppor flexible pytorch tensor communication.
+In recent update, we hide the communication details from user and provide simple APIs. :class:`DistNetwork` now provies two basic communication APIs: :meth:`send()` and :meth:`recv()`. These APIs support flexible pytorch tensor communication.
 
 **Sender process**:
 
@@ -80,7 +80,7 @@ In recent update, we hide the communication details from user and provide simple
 Further understanding of FedLab communication
 ================================================
 
-FedLab pack content into a pre-defined package data structure. :meth:`send()` and :meth:`recv()` are implemented like:
+FedLab packs content into a pre-defined package data structure. :meth:`send()` and :meth:`recv()` are implemented like:
 
 .. code-block:: python
 
@@ -123,7 +123,7 @@ Currently, you can create a network package from following methods:
     tensor_list = [torch.rand(size) for size in tensor_sizes]
     package = Package(content=tensor_list)
 
-3. append a tensor to exist package
+3. append a tensor to existing package
 
 .. code-block:: python
 
@@ -133,7 +133,7 @@ Currently, you can create a network package from following methods:
     new_tensor = torch.Tensor(size=(8,))
     package.append_tensor(new_tensor)
 
-4. append a tensor list to exist package
+4. append a tensor list to existing package
 
 .. code-block:: python
 

docs/source/tutorials/docker_deployment.rst

@@ -12,7 +12,7 @@ The communication APIs of **FedLab** is built on `torch.distributed <https://pyt
 Setup docker environment
 ==========================
 
-In this section, we introduce how to setup a docker image for **FedLab** program. Here we provide the Dockerfile for building a FedLab image. Our FedLab environment is based on PytTorch. Therefore, we just need install **FedLab** on the provided PytTorch image.
+In this section, we introduce how to setup a docker image for **FedLab** program. Here we provide the Dockerfile for building a FedLab image. Our FedLab environment is based on PytTorch. Therefore, we just need install **FedLab** on the provided PyTorch image.
 
 Dockerfile:
 
@@ -45,9 +45,9 @@ Dockerfile for different platforms
 
 The steps of modifying Dockerfile for different platforms:
 
-- **Step 1:** Find an appropriate base pytorch image for your platform from dockerhub https://hub.docker.com/r/pytorch/pytorch/tags. Then, replace the value of `TORCH_CONTAINER` in demo dockerfile.
+- **Step 1:** Find an appropriate base PyTorch image for your platform from dockerhub https://hub.docker.com/r/pytorch/pytorch/tags. Then, replace the value of `TORCH_CONTAINER` in demo dockerfile.
 
-- **Step 2:** To install specific PyTorch version, you need to choose a correct install command, which can be find in https://pytorch.org/get-started/previous-versions/. Then, modify the 16-th command in demo dockerfile.
+- **Step 2:** To install specific PyTorch version, you need to choose a correct install command, which can be found at https://pytorch.org/get-started/previous-versions/. Then, modify the 16-th command in demo dockerfile.
 
 - **Step 3:** Build the images for your own platform by running the command below in the dir of Dockerfile.
 
@@ -57,12 +57,12 @@ The steps of modifying Dockerfile for different platforms:
 
 .. warning::
 
-    Using "--gpus all" and "--network=host" when start a docker container:
+    Be sure to use "--gpus all" and "--network=host" when starting a docker container:
 
     .. code-block:: shell-session
 
         $ docker run -itd --gpus all --network=host b23a9c46cd04(image name) /bin/bash
 
-    If you are not in China area, it is ok to remove line 11,12 and "-i https://pypi.mirrors.ustc.edu.cn/simple/" in line 19.
+    If you are not in China area, it is ok to remove line 11, 12 and "-i https://pypi.mirrors.ustc.edu.cn/simple/" in line 19.
 
 - **Finally:** Run your FedLab process in the different started containers.